Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey

2025-04-30

AI Summary

This study addresses the bottleneck of multi-agent collaborative perception in V2X communications by proposing a novel paradigm that treats the wireless channel as a dynamic “information sensor.” To tackle practical challenges—including communication constraints, node heterogeneity, high mobility, and large-scale deployment—the work systematically develops: (1) information representations across data, feature, and objective levels; (2) a robust fusion mechanism mitigating localization errors, latency, and packet loss; and (3) a lightweight compression scheme with a scalable system architecture. It establishes, for the first time, an information-centric taxonomy for collaborative perception, unifying the modeling and processing of information flow in V2X sensing. The framework provides both theoretical foundations and technical enablers for highly reliable, scalable collaborative perception in intelligent connected vehicles operating under complex traffic conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Agent Communication
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi-agent information sharing via Vehicle-to-Everything (V2X) communication. Unlike traditional onboard sensors, V2X acts as a dynamic"information sensor"characterized by limited communication, heterogeneity, mobility, and scalability. This survey provides a comprehensive review of recent advancements from the perspective of information-centric cooperative perception, focusing on three key dimensions: information representation, information fusion, and large-scale deployment. We categorize information representation into data-level, feature-level, and object-level schemes, and highlight emerging methods for reducing data volume and compressing messages under communication constraints. In information fusion, we explore techniques under both ideal and non-ideal conditions, including those addressing heterogeneity, localization errors, latency, and packet loss. Finally, we summarize system-level approaches to support scalability in dense traffic scenarios. Compared with existing surveys, this paper introduces a new perspective by treating V2X communication as an information sensor and emphasizing the challenges of deploying cooperative perception in real-world intelligent transportation systems.

Problem

• Extends autonomous vehicle perception via multi-agent V2X communication
• Addresses challenges of limited communication, heterogeneity, and scalability
• Reviews information representation, fusion, and large-scale deployment techniques

Innovation

• V2X communication as dynamic information sensor
• Information fusion under non-ideal conditions
• System-level approaches for scalability